OpenRaiser/Intern-Atlas huggingface dataset:
Revisit the Imbalance Optimization in Multi-task Learning: An Experimental Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Multi-task learning (MTL) aims to build general-purpose vision systems by training a single network to perform multiple tasks jointly. While promising, its potential is often hindered by "unbalanced optimization", where task interference leads to subpar performance compared to single-task models. To facilitate research in MTL, this paper presents a systematic experimental analysis to dissect the factors contributing to this persistent problem. Our investigation confirms that the performance of existing optimization methods varies inconsistently across datasets, and advanced architectures still rely on costly grid-searched loss weights. Furthermore, we show that while powerful Vision Foundation Models (VFMs) provide strong initialization, they do not inherently resolve the optimization imbalance, and merely increasing data quantity offers limited benefits. A crucial finding emerges from our analysis: a strong correlation exists between the optimization imbalance and the norm of task-specific gradients. We demonstrate that this insight is directly applicable, showing that a straightforward strategy of scaling task losses according to their gradient norms can achieve performance comparable to that of an extensive and computationally expensive grid search. Our comprehensive analysis suggests that understanding and controlling gradient dynamics is a more direct path to stable MTL than developing increasingly complex methods.
Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models
Venue: arXiv cs.CV 2025
Year: 2025

Text-guided image inpainting aims to inpaint masked image regions based on a textual prompt while preserving the background. Although diffusion-based methods have become dominant, their property of modeling the entire image in latent space makes it challenging for the results to align well with prompt details and maintain a consistent background. To address these issues, we explore Mask AutoRegressive (MAR) models for this task. MAR naturally supports image inpainting by generating latent tokens corresponding to mask regions, enabling better local controllability without altering the background. However, directly applying MAR to this task makes the inpainting content either ignore the prompts or be disharmonious with the background context. Through analysis of the attention maps from the inpainting images, we identify the impact of background tokens on text tokens during the MAR generation, and leverage this to design \textbf{Token Painter}, a training-free text-guided image inpainting method based on MAR. Our approach introduces two key components: (1) Dual-Stream Encoder Information Fusion (DEIF), which fuses the semantic and context information from text and background in frequency domain to produce novel guidance tokens, allowing MAR to generate text-faithful inpainting content while keeping harmonious with background context. (2) Adaptive Decoder Attention Score Enhancing (ADAE), which adaptively enhances attention scores on guidance tokens and inpainting tokens to further enhance the alignment of prompt details and the content visual quality. Extensive experiments demonstrate that our training-free method outperforms prior state-of-the-art methods across almost all metrics. Codes: https://github.com/longtaojiang/Token-Painter.
Performance Analysis of Zero-Forcing Beamforming Strategies for the Uplink of an MU-MIMO System with Multi-Antenna Users
Venue: arXiv cs.NI 2025
Year: 2025

We conduct a comprehensive evaluation of the performance of the uplink of OFDMA-based MU-MIMO systems with multi-antenna users, for three Zero-Forcing (ZF) Beamforming (BF) strategies: Coordinated-Transmit-Receive-1 (CTR1), where only the strongest data stream is enabled per scheduled user; Block Diagonalization (BD), where all possible streams are enabled per scheduled user; Coordinated-Transmit-Receive-Flexible (CTRF), which allows a flexible stream allocation per user. The Radio Resource Management (RRM) of the uplink of all OFDMA-based systems must be done over an entire Time-Slot (TS) due to power management, making it challenging. To enable this study, we propose an efficient heuristic based on greedy-up searches for stream-sets that provides feasible solutions. It operates over the TS and considers fairness, practical Modulation and Coding Schemes and all RRM processes. The results show that, for Rural Macro scenarios, BD (resp. CTR1) could replace the more complex CTRF if the number of users is small (resp. large), while for Urban Macro scenarios, CTR1 emerges as an alternative to CTRF due to its similar performance. We also show that the system parameters can substantially impact the performance of the ZF strategies and that BD performance is more impaired with a simpler power management scheme than CTR1 and CTRF.
Beyond Benchmarks: Understanding Mixture-of-Experts Models through Internal Mechanisms
Venue: arXiv cs.LG 2025
Year: 2025

Mixture-of-Experts (MoE) architectures have emerged as a promising direction, offering efficiency and scalability by activating only a subset of parameters during inference. However, current research remains largely performance-centric, with limited understanding of its internal mechanisms, thereby constraining broader progress. In this work, we use an internal metric to investigate the mechanisms of MoE architecture by explicitly incorporating routing mechanisms and analyzing expert-level behaviors. Through systematic analyses of a wide range of publicly available MoE models, we uncover several findings: (1) neuron utilization decreases as models evolve, reflecting stronger generalization; (2) training exhibits a dynamic trajectory, where benchmark performance alone provides limited signal while MUI reveals deeper insights; (3) task completion emerges from collaborative contributions of multiple experts, with shared experts driving concentration; and (4) activation patterns at the neuron level provide a fine-grained proxy for data diversity. Together, these results demonstrate the potential of MUI as a complementary indicator to benchmark performance, offering new insights into the capacity, dynamics, and specialization of MoE models. Our project can be found at https://yingjiahao14.github.io/MoE-MUI/.
Efficient Identification of High Similarity Clusters in Polygon Datasets
Venue: arXiv cs.LG 2025
Year: 2025

Advancements in tools like Shapely 2.0 and Triton can significantly improve the efficiency of spatial similarity computations by enabling faster and more scalable geometric operations. However, for extremely large datasets, these optimizations may face challenges due to the sheer volume of computations required. To address this, we propose a framework that reduces the number of clusters requiring verification, thereby decreasing the computational load on these systems. The framework integrates dynamic similarity index thresholding, supervised scheduling, and recall-constrained optimization to efficiently identify clusters with the highest spatial similarity while meeting user-defined precision and recall requirements. By leveraging Kernel Density Estimation (KDE) to dynamically determine similarity thresholds and machine learning models to prioritize clusters, our approach achieves substantial reductions in computational cost without sacrificing accuracy. Experimental results demonstrate the scalability and effectiveness of the method, offering a practical solution for large-scale geospatial analysis.
VFSI: Validity First Spatial Intelligence for Constraint-Guided Traffic Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Modern diffusion models generate realistic traffic simulations but systematically violate physical constraints. In a large-scale evaluation of SceneDiffuser++, a state-of-the-art traffic simulator, we find that 50% of generated trajectories violate basic physical laws - vehicles collide, drive off roads, and spawn inside buildings. This reveals a fundamental limitation: current models treat physical validity as an emergent property rather than an architectural requirement. We propose Validity-First Spatial Intelligence (VFSI), which enforces constraints through energy-based guidance during diffusion sampling, without model retraining. By incorporating collision avoidance and kinematic constraints as energy functions, we guide the denoising process toward physically valid trajectories. Across 200 urban scenarios from the Waymo Open Motion Dataset, VFSI reduces collision rates by 67% (24.6% to 8.1%) and improves overall validity by 87% (50.3% to 94.2%), while simultaneously improving realism metrics (ADE: 1.34m to 1.21m). Our model-agnostic approach demonstrates that explicit constraint enforcement during inference is both necessary and sufficient for physically valid traffic simulation.
Automatic selection of primary studies in systematic reviews with evolutionary rule-based classification
Venue: arXiv cs.AI 2025
Year: 2025

Searching, filtering and analysing scientific literature are time-consuming tasks when performing a systematic literature review. With the rise of artificial intelligence, some steps in the review process are progressively being automated. In particular, machine learning for automatic paper selection can greatly reduce the effort required to identify relevant literature in scientific databases. We propose an evolutionary machine learning approach, called \ourmodel, to automatically determine whether a paper retrieved from a literature search process is relevant. \ourmodel builds an interpretable rule-based classifier using grammar-guided genetic programming. The use of a grammar to define the syntax and the structure of the rules allows \ourmodel to easily combine the usual textual information with other bibliometric data not considered by state-of-the-art methods. Our experiments demonstrate that it is possible to generate accurate classifiers without impairing interpretability and using configurable information sources not supported so far.
Generalized Category Discovery in Hyperspectral Images via Prototype Subspace Modeling
Venue: arXiv cs.CV 2025
Year: 2025

Generalized category discovery~(GCD) seeks to jointly identify both known and novel categories in unlabeled data. While prior works have mainly focused on RGB images, their assumptions and modeling strategies do not generalize well to hyperspectral images~(HSI), which are inherently high-dimensional and exhibit complex spectral structures. In this paper, we propose the first GCD framework tailored for HSI, introducing a prototype subspace modeling model to better capture class structure. Instead of learning a single prototype vector for each category as in existing methods such as SimGCD, we model each category using a set of basis vectors, forming a subspace representation that enables greater expressiveness and discrimination in a high-dimensional feature space. To guide the learning of such bases, we enforce two key constraints: (1) a basis orthogonality constraint that promotes inter-class separability, and (2) a reconstruction constraint that ensures each prototype basis can effectively reconstruct its corresponding class samples. Experimental results on real-world HSI demonstrate that our method significantly outperforms state-of-the-art GCD methods, establishing a strong foundation for generalized category discovery in hyperspectral settings.
$\mathbf{R}^3$: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
Venue: arXiv cs.CV 2025
Year: 2025

Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signal-processing pipeline irreversibly mixes colors, clips dynamic range, and blurs fine detail. This paper uses the rain degradation problem as a use case to show that these losses are avoidable, and demonstrates that learning directly on raw Bayer mosaics yields superior reconstructions. To substantiate the claim, we (i) evaluate post-ISP and Bayer reconstruction pipelines, (ii) curate Raw-Rain, the first public benchmark of real rainy scenes captured in both 12-bit Bayer and bit-depth-matched sRGB, and (iii) introduce Information Conservation Score (ICS), a color-invariant metric that aligns more closely with human opinion than PSNR or SSIM. On the test split, our raw-domain model improves sRGB results by up to +0.99 dB PSNR and +1.2% ICS, while running faster with half of the GFLOPs. The results advocate an ISP-last paradigm for low-level vision and open the door to end-to-end learnable camera pipelines.
TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents
Venue: arXiv cs.DC 2025
Year: 2025

Agent-based simulation is an indispensable paradigm for studying complex systems. These systems can comprise billions of agents, requiring the computing resources of multiple servers to simulate. Unfortunately, the state-of-the-art platform, BioDynaMo, does not scale out across servers due to its shared-memory-based implementation. To overcome this key limitation, we introduce TeraAgent, a distributed agent-based simulation engine. A critical challenge in distributed execution is the exchange of agent information across servers, which we identify as a major performance bottleneck. We propose two solutions: 1) a tailored serialization mechanism that allows agents to be accessed and mutated directly from the receive buffer, and 2) leveraging the iterative nature of agent-based simulations to reduce data transfer with delta encoding. Built on our solutions, TeraAgent enables extreme-scale simulations with half a trillion agents (an 84x improvement), reduces time-to-result with additional compute nodes, improves interoperability with third-party tools, and provides users with more hardware flexibility.
Demographic-Agnostic Fairness without Harm
Venue: arXiv cs.LG 2025
Year: 2025

As machine learning (ML) algorithms are increasingly used in social domains to make predictions about humans, there is a growing concern that these algorithms may exhibit biases against certain social groups. Numerous notions of fairness have been proposed in the literature to measure the unfairness of ML. Among them, one class that receives the most attention is \textit{parity-based}, i.e., achieving fairness by equalizing treatment or outcomes for different social groups. However, achieving parity-based fairness often comes at the cost of lowering model accuracy and is undesirable for many high-stakes domains like healthcare. To avoid inferior accuracy, a line of research focuses on \textit{preference-based} fairness, under which any group of individuals would experience the highest accuracy and collectively prefer the ML outcomes assigned to them if they were given the choice between various sets of outcomes. However, these works assume individual demographic information is known and fully accessible during training. In this paper, we relax this requirement and propose a novel \textit{demographic-agnostic fairness without harm (DAFH)} optimization algorithm, which jointly learns a group classifier that partitions the population into multiple groups and a set of decoupled classifiers associated with these groups. Theoretically, we conduct sample complexity analysis and show that our method can outperform the baselines when demographic information is known and used to train decoupled classifiers. Experiments on both synthetic and real data validate the proposed method.
Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
Venue: arXiv cs.CL 2025
Year: 2025

Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet, the significance of sentiment analysis has grown across numerous industries such as marketing, politics, and customer service. Sentiment analysis is flawed, however, when applied to foreign languages, particularly when there is no labelled data to train models upon. In this study, we present a transformer ensemble model and a large language model (LLM) that employs sentiment analysis of other languages. We used multi languages dataset. Sentiment was then assessed for sentences using an ensemble of pre-trained sentiment analysis models: bert-base-multilingual-uncased-sentiment, and XLM-R. Our experimental results indicated that sentiment analysis performance was more than 86% using the proposed method.
Clebsch-Gordan Transformer: Fast and Global Equivariant Attention
Venue: arXiv cs.LG 2025
Year: 2025

The global attention mechanism is one of the keys to the success of transformer architecture, but it incurs quadratic computational costs in relation to the number of tokens. On the other hand, equivariant models, which leverage the underlying geometric structures of problem instance, often achieve superior accuracy in physical, biochemical, computer vision, and robotic tasks, at the cost of additional compute requirements. As a result, existing equivariant transformers only support low-order equivariant features and local context windows, limiting their expressiveness and performance. This work proposes Clebsch-Gordan Transformer, achieving efficient global attention by a novel Clebsch-Gordon Convolution on $\SO(3)$ irreducible representations. Our method enables equivariant modeling of features at all orders while achieving ${O}(N \log N)$ input token complexity. Additionally, the proposed method scales well with high-order irreducible features, by exploiting the sparsity of the Clebsch-Gordon matrix. Lastly, we also incorporate optional token permutation equivariance through either weight sharing or data augmentation. We benchmark our method on a diverse set of benchmarks including n-body simulation, QM9, ModelNet point cloud classification and a robotic grasping dataset, showing clear gains over existing equivariant transformers in GPU memory size, speed, and accuracy.
GANji: A Framework for Introductory AI Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

The comparative study of generative models often requires significant computational resources, creating a barrier for researchers and practitioners. This paper introduces GANji, a lightweight framework for benchmarking foundational AI image generation techniques using a dataset of 10,314 Japanese Kanji characters. It systematically compares the performance of a Variational Autoencoder (VAE), a Generative Adversarial Network (GAN), and a Denoising Diffusion Probabilistic Model (DDPM). The results demonstrate that while the DDPM achieves the highest image fidelity, with a Fréchet Inception Distance (FID) score of 26.2, its sampling time is over 2,000 times slower than the other models. The GANji framework is an effective and accessible tool for revealing the fundamental trade-offs between model architecture, computational cost, and visual quality, making it ideal for both educational and research purposes.
Beyond Magic Words: Sharpness-Aware Prompt Evolving for Robust Large Language Models with TARE
Venue: arXiv cs.CL 2025
Year: 2025

The performance of Large Language Models (LLMs) hinges on carefully engineered prompts. However, prevailing prompt optimization methods, ranging from heuristic edits and reinforcement learning to evolutionary search, primarily target point-wise accuracy. They seldom enforce paraphrase invariance or searching stability, and therefore cannot remedy this brittleness in practice. Automated prompt search remains brittle: small, semantically preserving paraphrases often cause large performance swings. We identify this brittleness as the textual sharpness of the prompt landscape. In this work, we provide the first formal treatment of textual sharpness in the discrete, semantic space of prompts, together with an operational robustness criterion over a semantic neighborhood; the design is black-box or API-only, requiring no gradients to update the model's parameters. Then we introduce TARE (Textual Sharpness-Aware Evolving), a derivative-free framework that alternates between an inner, sampling-based adversarial search that stresses a prompt with hard paraphrases and an outer, robust selection that prefers candidates whose neighborhoods remain strong. We further propose ATARE, which learns anisotropic weights to shape the semantic neighborhood and adapts its radius over time to balance exploration and fidelity. Diverse tasks evaluate our methods, whose design for minimizing textual sharpness gap leads to prompts that preserve accuracy under paraphrasing, outperforming accuracy-only prompt search while remaining computationally practical.
A signal separation view of classification
Venue: arXiv cs.LG 2025
Year: 2025

The problem of classification in machine learning has often been approached in terms of function approximation. In this paper, we propose an alternative approach for classification in arbitrary compact metric spaces which, in theory, yields both the number of classes, and a perfect classification using a minimal number of queried labels. Our approach uses localized trigonometric polynomial kernels initially developed for the point source signal separation problem in signal processing. Rather than point sources, we argue that the various classes come from different probability distributions. The localized kernel technique developed for separating point sources is then shown to separate the supports of these distributions. This is done in a hierarchical manner in our MASC algorithm to accommodate touching/overlapping class boundaries. We illustrate our theory on several simulated and real life datasets, including the Salinas and Indian Pines hyperspectral datasets and a document dataset.
DNS in the Time of Curiosity: A Tale of Collaborative User Privacy Protection
Venue: arXiv cs.CR 2025
Year: 2025

The Domain Name System (DNS) is central to all Internet user activity, resolving accessed domain names into Internet Protocol (IP) addresses. As a result, curious DNS resolvers can learn everything about Internet users' interests. Public DNS resolvers are rising in popularity, offering low-latency resolution, high reliability, privacy-preserving policies, and support for encrypted DNS queries. However, client-resolver traffic encryption, increasingly deployed to protect users from eavesdroppers, does not protect users against curious resolvers. Similarly, privacy-preserving policies are based solely on written commitments and do not provide technical safeguards. Although DNS query relay schemes can separate duties to limit data accessible by each entity, they cannot prevent colluding entities from sharing user traffic logs. Thus, a key challenge remains: organizations operating public DNS resolvers, accounting for the majority of DNS resolutions, can potentially collect and analyze massive volumes of Internet user activity data. With DNS infrastructure that cannot be fully trusted, can we safeguard user privacy? We answer positively and advocate for a user-driven approach to reduce exposure to DNS services. We will discuss key ideas of the proposal, which aims to achieve a high level of privacy without sacrificing performance: maintaining low latency, network bandwidth, memory/storage overhead, and computational overhead.